Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes.

ABSTRACT: Learning risk scores to predict dichotomous or continuous outcomes using machine learning approaches has been studied extensively. However, how to learn risk scores for time-to-event outcomes subject to right censoring has received little attention until recently. Existing approaches rely on inverse probability weighting or rank-based regression, which may be inefficient. In this paper, we develop a new support vector hazards machine (SVHM) approach to predict censored outcomes. Our method is based on predicting the counting process associated with the time-to-event outcomes among subjects at risk via a series of support vector machines. Introducing counting processes to represent time-to-event data leads to a connection between support vector machines in supervised learning and hazards re

SUBMITTER: Wang Y 

PROVIDER: S-EPMC5210213 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature